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A new customer churn prediction approach based on soft set ensemble pruning

Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low....

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Main Authors: Mohd Khalid, Awang, Mohd Nordin, Abdul Rahman, Mokhairi, Makhtar, Mustafa, Mat Deris
Format: Conference or Workshop Item
Language:English
Published: 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.unisza.edu.my/1689/1/FH03-FIK-17-08102.jpg
http://eprints.unisza.edu.my/1689/
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spelling my-unisza-ir.16892020-11-19T07:39:59Z http://eprints.unisza.edu.my/1689/ A new customer churn prediction approach based on soft set ensemble pruning Mohd Khalid, Awang Mohd Nordin, Abdul Rahman Mokhairi, Makhtar Mustafa, Mat Deris QA75 Electronic computers. Computer science Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn. 2017 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/1689/1/FH03-FIK-17-08102.jpg Mohd Khalid, Awang and Mohd Nordin, Abdul Rahman and Mokhairi, Makhtar and Mustafa, Mat Deris (2017) A new customer churn prediction approach based on soft set ensemble pruning. In: The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016;, 18-20 August 2016, Bandung; Indonesia.
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohd Khalid, Awang
Mohd Nordin, Abdul Rahman
Mokhairi, Makhtar
Mustafa, Mat Deris
A new customer churn prediction approach based on soft set ensemble pruning
description Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn.
format Conference or Workshop Item
author Mohd Khalid, Awang
Mohd Nordin, Abdul Rahman
Mokhairi, Makhtar
Mustafa, Mat Deris
author_facet Mohd Khalid, Awang
Mohd Nordin, Abdul Rahman
Mokhairi, Makhtar
Mustafa, Mat Deris
author_sort Mohd Khalid, Awang
title A new customer churn prediction approach based on soft set ensemble pruning
title_short A new customer churn prediction approach based on soft set ensemble pruning
title_full A new customer churn prediction approach based on soft set ensemble pruning
title_fullStr A new customer churn prediction approach based on soft set ensemble pruning
title_full_unstemmed A new customer churn prediction approach based on soft set ensemble pruning
title_sort new customer churn prediction approach based on soft set ensemble pruning
publishDate 2017
url http://eprints.unisza.edu.my/1689/1/FH03-FIK-17-08102.jpg
http://eprints.unisza.edu.my/1689/
_version_ 1684657737720397824
score 13.211869

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